Overview

Dataset statistics

Number of variables17
Number of observations804
Missing cells18
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory106.9 KiB
Average record size in memory136.2 B

Variable types

NUM9
BOOL4
CAT3
UNSUPPORTED1

Reproduction

Analysis started2020-08-30 07:05:02.541481
Analysis finished2020-08-30 07:05:32.564766
Duration30.02 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

ID has unique values Unique
Education_loan is an unsupported type, check if it needs cleaning or further analysis Unsupported
Emp_duration has 30 (3.7%) zeros Zeros

Variables

ID
Real number (ℝ≥0)

UNIQUE

Distinct count804
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.5
Minimum101
Maximum904
Zeros0
Zeros (%)0.0%
Memory size6.3 KiB
2020-08-30T12:35:32.697667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile141.15
Q1301.75
median502.5
Q3703.25
95-th percentile863.85
Maximum904
Range803
Interquartile range (IQR)401.5

Descriptive statistics

Standard deviation232.2391009
Coefficient of variation (CV)0.462167365
Kurtosis-1.2
Mean502.5
Median Absolute Deviation (MAD)201
Skewness0
Sum404010
Variance53935
2020-08-30T12:35:32.888815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
90410.1%
 
36410.1%
 
37410.1%
 
37310.1%
 
37210.1%
 
37110.1%
 
37010.1%
 
36910.1%
 
36810.1%
 
36710.1%
 
Other values (794)79498.8%
 
ValueCountFrequency (%) 
10110.1%
 
10210.1%
 
10310.1%
 
10410.1%
 
10510.1%
 
ValueCountFrequency (%) 
90410.1%
 
90310.1%
 
90210.1%
 
90110.1%
 
90010.1%
 

Default
Boolean

Distinct count2
Unique (%)0.2%
Missing1
Missing (%)0.1%
Memory size6.3 KiB
No
568
Yes
235
(Missing)
 
1
ValueCountFrequency (%) 
No56870.6%
 
Yes23529.2%
 
(Missing)10.1%
 

Checking_amount
Real number (ℝ)

Distinct count584
Unique (%)72.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean370.07098381070983
Minimum-436.0
Maximum1319.0
Zeros0
Zeros (%)0.0%
Memory size6.3 KiB
2020-08-30T12:35:33.074437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-436
5-th percentile-108.9
Q1165.5
median357
Q3567
95-th percentile886.5
Maximum1319
Range1755
Interquartile range (IQR)401.5

Descriptive statistics

Standard deviation301.8784477
Coefficient of variation (CV)0.8157312
Kurtosis-0.04827371015
Mean370.0709838
Median Absolute Deviation (MAD)199
Skewness0.2079262984
Sum297167
Variance91130.5972
2020-08-30T12:35:33.267110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
37550.6%
 
5840.5%
 
29740.5%
 
23140.5%
 
1640.5%
 
44540.5%
 
25530.4%
 
17030.4%
 
56830.4%
 
-4430.4%
 
Other values (574)76695.3%
 
ValueCountFrequency (%) 
-43610.1%
 
-40710.1%
 
-38610.1%
 
-38310.1%
 
-37910.1%
 
ValueCountFrequency (%) 
131910.1%
 
129610.1%
 
127510.1%
 
121710.1%
 
121310.1%
 

Term
Real number (ℝ≥0)

Distinct count19
Unique (%)2.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean17.835616438356166
Minimum9.0
Maximum27.0
Zeros0
Zeros (%)0.0%
Memory size6.3 KiB
2020-08-30T12:35:33.448306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12
Q116
median18
Q320
95-th percentile23
Maximum27
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.266680075
Coefficient of variation (CV)0.1831548737
Kurtosis-0.09089679072
Mean17.83561644
Median Absolute Deviation (MAD)2
Skewness0.02861842049
Sum14322
Variance10.67119872
2020-08-30T12:35:33.631149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1810913.6%
 
179812.2%
 
199211.4%
 
16739.1%
 
20729.0%
 
21698.6%
 
15678.3%
 
14496.1%
 
22364.5%
 
13344.2%
 
Other values (9)10412.9%
 
ValueCountFrequency (%) 
930.4%
 
1070.9%
 
11111.4%
 
12212.6%
 
13344.2%
 
ValueCountFrequency (%) 
2730.4%
 
2650.6%
 
25131.6%
 
24151.9%
 
23263.2%
 

Credit_score
Real number (ℝ≥0)

Distinct count276
Unique (%)34.4%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean759.5960099750623
Minimum376.0
Maximum1029.0
Zeros0
Zeros (%)0.0%
Memory size6.3 KiB
2020-08-30T12:35:33.823134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum376
5-th percentile610
Q1725
median770
Q3811
95-th percentile860
Maximum1029
Range653
Interquartile range (IQR)86

Descriptive statistics

Standard deviation76.49096547
Coefficient of variation (CV)0.1006995356
Kurtosis1.961721091
Mean759.59601
Median Absolute Deviation (MAD)43
Skewness-0.879983214
Sum609196
Variance5850.867799
2020-08-30T12:35:34.001858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
771121.5%
 
773101.2%
 
81391.1%
 
76691.1%
 
75691.1%
 
79991.1%
 
76381.0%
 
81581.0%
 
84481.0%
 
77281.0%
 
Other values (266)71288.6%
 
ValueCountFrequency (%) 
37610.1%
 
45110.1%
 
46910.1%
 
51220.2%
 
51810.1%
 
ValueCountFrequency (%) 
102910.1%
 
99110.1%
 
97410.1%
 
96210.1%
 
94110.1%
 

Gender
Categorical

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
Male
518
Female
286
ValueCountFrequency (%) 
Male51864.4%
 
Female28635.6%
 
2020-08-30T12:35:34.224660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.711442786
Min length4

Marital_status
Categorical

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
Single
421
Married
383
ValueCountFrequency (%) 
Single42152.4%
 
Married38347.6%
 
2020-08-30T12:35:34.461896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.476368159
Min length6

Car_loan
Boolean

Distinct count2
Unique (%)0.2%
Missing1
Missing (%)0.1%
Memory size6.3 KiB
No
540
Yes
263
(Missing)
 
1
ValueCountFrequency (%) 
No54067.2%
 
Yes26332.7%
 
(Missing)10.1%
 
Distinct count2
Unique (%)0.2%
Missing3
Missing (%)0.4%
Memory size6.3 KiB
Yes
408
No
393
(Missing)
 
3
ValueCountFrequency (%) 
Yes40850.7%
 
No39348.9%
 
(Missing)30.4%
 

Home_loan
Boolean

Distinct count2
Unique (%)0.2%
Missing2
Missing (%)0.2%
Memory size6.3 KiB
No
760
Yes
 
42
(Missing)
 
2
ValueCountFrequency (%) 
No76094.5%
 
Yes425.2%
 
(Missing)20.2%
 

Education_loan
Unsupported

REJECTED
UNSUPPORTED

Missing1
Missing (%)0.1%
Memory size6.4 KiB

Emp_status
Categorical

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
unemployed
503
employed
301
ValueCountFrequency (%) 
unemployed50362.6%
 
employed30137.4%
 
2020-08-30T12:35:34.704646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.251243781
Min length8

Amount
Real number (ℝ≥0)

Distinct count573
Unique (%)71.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1217.6301369863013
Minimum244.0
Maximum2362.0
Zeros0
Zeros (%)0.0%
Memory size6.3 KiB
2020-08-30T12:35:34.888568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum244
5-th percentile717.2
Q11007.5
median1224
Q31422
95-th percentile1719
Maximum2362
Range2118
Interquartile range (IQR)414.5

Descriptive statistics

Standard deviation308.279582
Coefficient of variation (CV)0.2531799868
Kurtosis-0.008605283972
Mean1217.630137
Median Absolute Deviation (MAD)205
Skewness-0.02283736831
Sum977757
Variance95036.30069
2020-08-30T12:35:35.027558image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
106250.6%
 
108550.6%
 
128140.5%
 
128640.5%
 
114640.5%
 
142940.5%
 
116240.5%
 
78330.4%
 
111130.4%
 
122430.4%
 
Other values (563)76495.0%
 
ValueCountFrequency (%) 
24410.1%
 
31610.1%
 
38510.1%
 
38610.1%
 
39510.1%
 
ValueCountFrequency (%) 
236210.1%
 
206610.1%
 
205210.1%
 
198310.1%
 
198210.1%
 

Saving_amount
Real number (ℝ≥0)

Distinct count594
Unique (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3174.814676616915
Minimum2082
Maximum4108
Zeros0
Zeros (%)0.0%
Memory size6.3 KiB
2020-08-30T12:35:35.179440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2082
5-th percentile2615.15
Q12948.75
median3200
Q33395
95-th percentile3715.55
Maximum4108
Range2026
Interquartile range (IQR)446.25

Descriptive statistics

Standard deviation340.2855686
Coefficient of variation (CV)0.1071828133
Kurtosis-0.08647134084
Mean3174.814677
Median Absolute Deviation (MAD)227
Skewness-0.1026719465
Sum2552551
Variance115794.2682
2020-08-30T12:35:35.547464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
339140.5%
 
318340.5%
 
292940.5%
 
345940.5%
 
327340.5%
 
328240.5%
 
338440.5%
 
319930.4%
 
323730.4%
 
314530.4%
 
Other values (584)76795.4%
 
ValueCountFrequency (%) 
208210.1%
 
214510.1%
 
219120.2%
 
229010.1%
 
235010.1%
 
ValueCountFrequency (%) 
410810.1%
 
404410.1%
 
402210.1%
 
402110.1%
 
401410.1%
 

Emp_duration
Real number (ℝ≥0)

ZEROS

Distinct count121
Unique (%)15.1%
Missing3
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean47.561797752808985
Minimum0.0
Maximum120.0
Zeros30
Zeros (%)3.7%
Memory size6.3 KiB
2020-08-30T12:35:35.733564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114
median39
Q379
95-th percentile114
Maximum120
Range120
Interquartile range (IQR)65

Descriptive statistics

Standard deviation37.13992446
Coefficient of variation (CV)0.7808772211
Kurtosis-1.095347249
Mean47.56179775
Median Absolute Deviation (MAD)29
Skewness0.4758560852
Sum38097
Variance1379.373989
2020-08-30T12:35:35.904051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0303.7%
 
10172.1%
 
5172.1%
 
6151.9%
 
1151.9%
 
42141.7%
 
11141.7%
 
21141.7%
 
22131.6%
 
12131.6%
 
Other values (111)63979.5%
 
ValueCountFrequency (%) 
0303.7%
 
1151.9%
 
2111.4%
 
3121.5%
 
4121.5%
 
ValueCountFrequency (%) 
12070.9%
 
11970.9%
 
11850.6%
 
11760.7%
 
11660.7%
 

Age
Real number (ℝ≥0)

Distinct count25
Unique (%)3.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean31.273972602739725
Minimum18.0
Maximum42.0
Zeros0
Zeros (%)0.0%
Memory size6.3 KiB
2020-08-30T12:35:36.094003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q129
median32
Q334
95-th percentile38
Maximum42
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.096060699
Coefficient of variation (CV)0.1309734696
Kurtosis-0.1453879945
Mean31.2739726
Median Absolute Deviation (MAD)3
Skewness-0.2347842426
Sum25113
Variance16.77771325
2020-08-30T12:35:36.241851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
329511.8%
 
33769.5%
 
30718.8%
 
31678.3%
 
29637.8%
 
35607.5%
 
34556.8%
 
28486.0%
 
36475.8%
 
27425.2%
 
Other values (15)17922.3%
 
ValueCountFrequency (%) 
1810.1%
 
1910.1%
 
2030.4%
 
2130.4%
 
2281.0%
 
ValueCountFrequency (%) 
4220.2%
 
4130.4%
 
4040.5%
 
39151.9%
 
38222.7%
 

No_of_credit_acc
Real number (ℝ≥0)

Distinct count9
Unique (%)1.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.4022415940224158
Minimum1.0
Maximum9.0
Zeros0
Zeros (%)0.0%
Memory size6.3 KiB
2020-08-30T12:35:36.432587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.536373603
Coefficient of variation (CV)0.6395583219
Kurtosis2.748836272
Mean2.402241594
Median Absolute Deviation (MAD)1
Skewness1.498095834
Sum1929
Variance2.360443847
2020-08-30T12:35:36.602121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
228535.4%
 
126132.5%
 
48110.1%
 
5799.8%
 
3799.8%
 
981.0%
 
740.5%
 
830.4%
 
630.4%
 
(Missing)10.1%
 
ValueCountFrequency (%) 
126132.5%
 
228535.4%
 
3799.8%
 
48110.1%
 
5799.8%
 
ValueCountFrequency (%) 
981.0%
 
830.4%
 
740.5%
 
630.4%
 
5799.8%
 

Interactions

2020-08-30T12:35:08.137175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:08.492546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:08.782990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:09.072283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:09.349857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:09.608628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:09.910610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:10.256459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:10.588921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:10.921573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:11.230247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:11.546840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:11.896062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:12.244442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:12.562651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:12.849380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:13.127642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:13.389003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:13.672135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:13.957520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:14.245569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:14.524947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:14.799578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:15.056738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:15.344304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:15.622206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:15.984062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:16.270289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:16.549567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:16.826011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:17.098401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:17.364286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:17.605618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:17.894495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:18.160274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:18.410620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:18.687286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:18.934310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:19.187308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:19.435668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:19.678133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:19.903547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:20.159949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:20.400065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:20.627585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:20.876804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:21.172176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:21.462214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:21.754635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:22.042125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:22.311396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:22.612784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:22.897194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:23.171535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:23.468534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:23.744566image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:24.019663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:24.289914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:24.553346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:24.801682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:25.085494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:25.348407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:25.725111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:25.996700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:26.262736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:26.520819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:26.775473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:27.027311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:27.255715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:27.513334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:27.762002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:27.999523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:28.256762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:28.542002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:28.828747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:29.118841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:29.369515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:29.555437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:29.810782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:30.039568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:30.292301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-30T12:35:36.808517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-30T12:35:37.105539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-30T12:35:37.437160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-30T12:35:37.767024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-08-30T12:35:38.154086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-30T12:35:30.785342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:31.436355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:31.848721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-30T12:35:32.282767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

IDDefaultChecking_amountTermCredit_scoreGenderMarital_statusCar_loanPersonal_loanHome_loanEducation_loanEmp_statusAmountSaving_amountEmp_durationAgeNo_of_credit_acc
0101No988.015.0796.0FemaleSingleYesNoNoNoemployed1536.0345512.038.01.0
1102No458.015.0813.0FemaleSingleYesNoNoNoemployed947.0360025.036.01.0
2103No158.014.0756.0FemaleSingleNoYesNoNoemployed1678.0309343.034.01.0
3104Yes300.025.0737.0FemaleSingleNoNoNoYesemployed1804.024490.029.01.0
4105Yes63.024.0662.0FemaleSingleNoNoNoYesunemployed1184.028674.030.01.0
5106No1071.020.0828.0MaleMarriedYesNoNoNoemployed475.0328212.032.02.0
6107No-192.013.0856.0MaleSingleYesNoNoNoemployed626.0339811.038.01.0
7108No172.016.0763.0FemaleSingleYesNoNoNoemployed1224.0302212.036.01.0
8109No585.020.0778.0FemaleSingleYesNoNoNounemployed1162.0347512.036.01.0
9110Yes189.019.0649.0MaleMarriedYesNoNoNoemployed786.027110.029.01.0

Last rows

IDDefaultChecking_amountTermCredit_scoreGenderMarital_statusCar_loanPersonal_loanHome_loanEducation_loanEmp_statusAmountSaving_amountEmp_durationAgeNo_of_credit_acc
794895No142.013.0773.0MaleMarriedYesNoNo0unemployed1394.0313419.030.02.0
795896No145.021.0792.0MaleMarriedNoYesNo0unemployed1183.0288114.035.01.0
796897No414.022.0752.0MaleMarriedNoYesNo0unemployed1373.0316542.030.02.0
797898Yes85.020.0843.0MaleMarriedYesNoNo0unemployed1078.03212109.030.02.0
798899Yes-293.021.0818.0FemaleSingleYesNoNo0unemployed1002.029830.029.02.0
799900No393.018.0846.0FemaleSingleNoYesNo0unemployed1603.0328254.031.01.0
800901No462.021.0810.0FemaleSingleYesNoNo0unemployed1435.03873110.032.01.0
801902No717.017.0739.0MaleMarriedYesNoNo0unemployed1669.0345332.031.02.0
802903No822.017.0783.0MaleMarriedNoYesNo0unemployed1041.0331243.034.02.0
803904Yes512.018.0601.0MaleMarriedYesNoNo0unemployed997.03060104.026.01.0